Year: 2026
Type: conference-paper
Abstract: Clinical voice agents are now deployed in routine care, where real patients do not wait their turn: they interrupt.These systems typically use a cascaded architecture (speech-to-text → LLM → text-to-speech), so when a patient cuts the agent off mid-utterance, clinically required content can be lost even when the model handles cooperative transcripts well.Yet clinical conversational-AI benchmarks almost universally assume patients wait for the agent to finish, missing interruption-induced loss of required content.We present a transcript-based evaluation of interruption recovery, adapting conversation-analytic overlap categories into three operational types (recognitional, competitive, transitional sub-unit) and testing four deployment-oriented, non-reasoning LLM configurations across four cells spanning historytaking (information gathering) and FAQ (information provision), scored on whether the agent preserves the clinically required content.In the gathering cells, target-question failure varied across models; in the provision cells, where arms are directly comparable, failure rose for every model.Rankings differ across cells, and competitive FAQ interruption produced 30/30 provision-coverage failures for all four models (Wilson 95% CI: 88.6-100.0%;baseline 0/30 for three, 4/30 for Llama).A brief apology marker ("sorry to interrupt") shifts recovery by tens of percentage points, inconsistently across models, and for one it reduces recovery.Interruption robustness therefore cannot be a single score: evaluation must be content-grounded, reported per cell, and matched to the deployment's interruption profile. 1
Institution: Newcastle University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7204921765